Clever Algorithms: Nature-Inspired Programming Recipes

Easy-to-understand, with a few algorithms which i had some difficulty doing implementation in the past.

Evolutionary Design by Computation

A book that started me on the development of several interactive evolutionary computation programs, very enjoyable book to read as well :)

http://www0.cs.ucl.ac.uk/staff/ucacpjb/evdes.html

Reading Status: Completed

An Introduction to Genetic Algortihms for Scientists and Engineers

Another entry-level book for learning genetic algorithms

http://www.amazon.com/Introduction-Genetic-Algorithms-Scientists-Engineers/dp/9810236026

Reading Status: Completed

Applied Genetic Algorithm in Java

A starting entry-level book for learning genetic algorithm in Java. very easy to understand and implement.

http://www.springer.com/computer/ai/book/978-0-387-95568-1

Reading Status: Completed

Supply Chain Optimization Design and Management Advances and Intelligent Methods.

Very nice book about the recent supply chain optimization method as well as computational intelligence approach for solving problems such customer uncertainty, etc.
Reading Status: Not Completed

Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language (Genetic Programming)

The book I used to develop my own grammatical evolution library.

Link: http://www.amazon.com/Grammatical-Evolution-Evolutionary-Automatic-Programming/dp/1402074441

Reading Status: Completed

Linear Genetic Programming (Genetic and Evolutionary Computation)

The book I used to write my own Linear Genetic Programming library:

Link: http://www.amazon.com/Linear-Genetic-Programming-Evolutionary-Computation/dp/0387310290
Reading Status: Completed

A Field Guide to Genetic Programming

A Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions.

Link: http://www.gp-field-guide.org.uk/
Reading Status: Completed

Evolutionary Computation for Modeling and Optimization

Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming.  The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modeling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered.

This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool.

Link: http://www.springer.com/mathematics/computational+science+%26+engineering/book/978-0-387-22196-0

Reading Status: Completed